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IAES International Journal of Artificial Intelligence (IJ-AI)
Published by Institute of Advanced Engineering and Science
ISSN : 20894872     EISSN : 22528938     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc); reasoning and evolution; intelligence applications; computer vision and speech understanding; multimedia and cognitive informatics, data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning; technology and computing (like particle swarm optimization); intelligent system architectures; knowledge representation; bioinformatics; natural language processing; multiagent systems; etc.
Arjuna Subject : -
Articles 2,057 Documents
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Biometric authentication using dual-modal deep learning based-on electrocardiogram and ear features Mohamed S. Khalaf; Said Fathy Al-Zoghdy; Mariana Barsoum; Ibrahim Omara
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3252-3268

Abstract

Biometric authentication systems are essential for secure access control; however unimodal systems are vulnerable to spoofing and environmental variations. This study proposes a dual-modal biometric system combining electrocardiogram (ECG) signals and ear features to enhance security and accuracy. Deep learning architectures including VGG-verydeep16 and convolutional neural network 5 (CNN5) are evaluated for feature extraction and fusion. Experimental results show that hybrid model (VGG-verydeep16 + CNN5) achieves around 98% accuracy, outperforming individual models. The system adapts to dataset size, using CNN5 for large datasets and VGG-verydeep16 for smaller ones. This approach offers a robust, efficient, and scalable solution for real-world biometric authentication.
Real-time object detection for autonomous driving: a comparative study of YOLO and Faster R-CNN Madhura M. Bhosale; Yogesh S. Angal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3581-3590

Abstract

Over the past few years, object detection has experienced remarkable progress and development, primarily driven by the development of one-stage and two-stage detection algorithms. Among these, Faster region-based convolutional neural network (Faster R-CNN) and you only look once (YOLO) have achieved notable success due to their strong performance and computational efficiency. Object detection plays a crucial role in various applications, particularly in autonomous driving systems, where accurate detection of pedestrians, vehicles, and road signs is essential for ensuring safety and reliability. This paper conducts a comparative evaluation of YOLO and Faster R-CNN to analyze their performance in autonomous driving environments. The experiments were conducted using the KITTI open-source dataset, which is widely used for benchmarking object detection models. All experiments were performed on an NVIDIA RTX A5000 GPU to ensure efficient computation, with implementations developed using Python version 3.9.13. The experimental findings indicate that YOLO surpasses Faster R-CNN in performance, attaining an accuracy rate of 90%. These findings highlight the effectiveness of YOLO for real-time object detection tasks, making it a suitable and preferred choice for time-sensitive applications such as autonomous driving systems.
Social news factuality verification using large language models Tran Duc Duong; Hai Hoan Do
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3144-3153

Abstract

Social media platforms have greatly accelerated the spread of news, but this rapid information flow also amplifies the risk of misinformation. Traditional automatic detection methods that rely solely on textual features often struggle with nuanced, emerging content. This paper present a novel pipeline that verifies the factuality of social news by clustering related articles into events and using large language models (LLMs) to extract and verify claims against trusted news sources. The approach groups social-media posts and mainstream reports on the same event, extracts atomic claims with a model like GPT-4, and checks each claim’s truthfulness by comparing it to the cluster’s reliable news. This pipeline was evaluated on a newly constructed Vietnamese news dataset of 1,765 articles (including 723 social-media items), manually annotating claims as true or false. The LLM-based method achieved high accuracy (≈88.9% F1-score on claim verification and 92.1% F1-score on overall news verification). These results demonstrate that carefully prompted LLMs, combined with event-level clustering of evidence, can outperform traditional methods (e.g., bidirectional encoder representations from transformers (BERT)-based classifiers) in verifying news. The paper discusses advantages of clustering over simple retrieval, scalability considerations for LLMs, and prospects for multilingual and knowledge-enhanced verification.
Indian sign language video generation using attention-enhanced generative adversarial network Prachi Pramod Waghmare; Ashwini Mangesh Deshpande
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3672-3682

Abstract

Sign language (SL) is the primary mode of communication for the Deaf signers. Despite advancements in deep learning, SL recognition, translation, and video generation face challenges like blurriness and inconsistencies. This research proposes a novel sign language translation (SLT) approach for Indian sign language (ISL) using an attention-driven generative adversarial network (GAN). The preprocessing pipeline includes video frame extraction, skeletal joint coordinate detection via OpenPose, and dynamic time warping (DTW) for pose data refinement. The squeeze and excitation (SE) attention mechanism enhances 2D convolutional layers, allowing the generator to focus on relevant skeletal pose sequences. A motion discriminator refines motion authenticity. Performance evaluation using two SL datasets demonstrates significant improvements in structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and temporal consistency metric (TCM) scores, achieving 99.60 (%) as SSIM, 31.10 dB as PSNR, and 0.9111 as TCM. The proposed model outperforms standard GAN and dynamic GAN in SL video generation.
Comparative evaluation of transfer learning models and Grad-CAM interpretability for brain tumor detection from MRI Md. Firoz Hasan; Md. Awal Hadi; Sumaiya Nasrin; Md. Raisul Islam; Md. Atik Shahriar; Md. Hasan Moon; Tanvir Ahmed Momin; Dewan Mamun Raza
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3646-3659

Abstract

Brain tumor classification plays an important role in early diagnosis and treatment planning. The current study aims to evaluate and compare the performance of five pre-trained convolutional neural network (CNN) models, namely VGG16, VGG19, MobileNet, Xception, and InceptionV3 using magnetic resonance imaging (MRI) images categorized into glioma, meningioma, pituitary tumor, and no tumor classes. To enhance model performance and address class imbalance, transfer learning and data augmentation techniques were employed. To boost model interpretability, heatmaps of important areas in tumor classification were produced through gradient-weighted class activation mapping (Grad-CAM). MobileNet was the most accurate with 97% and was more precise and more sensitive. The Grad-CAM visualizations showed the models were attending to clinically relevant features, which increased the interpretability. This comparative study demonstrates the effectiveness of the integration of explainable artificial intelligence (XAI) in deep learning pipelines for reliable brain tumor diagnosis.
Transforming correspondence learning through a smart assistant system to improve student outcomes and engagement Marsofiyati Marsofiyati; Roni Faslah; Maulana Amirul Adha; Muhammad Ikhwan; Faerozh Bin Madli
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3218-3227

Abstract

The insufficient feedback in business correspondence education at the higher education level necessitates innovation in adaptive and responsive learning resources. This study seeks to create and evaluate the efficacy of an artificial intelligence (AI)-driven correspondence learning assistant (CORLA) to enhance students' correspondence abilities. The study employs a research and development (R&D) methodology by utilizing the Dick and Carey model, which has ten phases of instructional development. The participants in the study were students enrolled in Digital Office Administration Study Program, Universitas Negeri Jakarta. Data were gathered by observation, interviews, questionnaires, and assessments (pre-test and post-test), and evaluated by three experts in materials, learning design, and media. The findings indicated that CORLA demonstrates a significant degree of feasibility, practicality, and efficacy. CORLA's advanced features encompass AI-driven automatic feedback, letter composition simulation, learning progress monitoring, discussion forums, and an intuitive user interface (UI). This study advances the creation of an effective and practical AI-based learning media model tailored to the requirements of teaching business correspondence abilities in higher education.
Advances in current state of diagnostic approaches towards investigating pervasive development disorder Ambika Sriranga; Herur Rangaiah Ranganatha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3081-3089

Abstract

Pervasive development disorder (PDD) is characterized by various types of neurodevelopment conditions influencing communication skill, behavioral, and social skills. Current state of screening methods mainly uses neurophysiological and clinical assessments that are reliable but suffer from challenges e.g., scalability, increased cost, and subjectivity. Artificial intelligence (AI) based solution is witnessed to have increased adoption in this regard also suffers from clinical integration, data bias, and explainability-oriented problems despite its potential contribution till date. Therefore, the proposed study presents a systematic review of recent literature with a closer emphasis on AI and non-AI-based methodologies adopted to diagnose PDD. The study outcome also exhibits similar facts. The novelty of this work resides in its comparative analysis, temporal mapping of current trends, and its integrative framework. It also contributes towards identifying certain underexplored areas, thereby facilitating practically viable insight towards the future direction of implementation. The idea is to understand its viability, which is not only clinically relevant but also offers sustainable diagnostics.

Page 206 of 206 | Total Record : 2057

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